Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Editor's note: This article was originally published by Craig Riddell on LinkedIn. It has been republished here with the author's permission. Boards are giving AI security more airtime than ever. What they're not giving is the right framing. A year or two ago, AI was mostly a question of experimentation risk. Today, it's tied directly to revenue, customer experience, operational efficiency, and competitive advantage. The urgency is real, and it's translating into aggressive deployment timelines.

The Model Context Protocol (MCP) is a de facto standard for providing structured access to privileged systems for AI agents and external integrations. It acts as a USB-C port for AI, enabling faster innovation by allowing organizations to expose tools, resources, and workflows without the time-consuming work of building APIs. Adoption has surged in recent months, and categories like payments, project management, and developer platforms are already beginning to reap the benefits.

Most organizations treating AI security as a model problem are defending the wrong layer. Security teams filter prompts, patch jailbreaks, and tune model behavior, which is all necessary work, while the actual attack surface sits largely unexamined underneath. That surface is the API layer: the endpoints AI systems use to retrieve data, call tools, and take action on behalf of users. This isn't a theoretical gap.

Every secure API draws a line between code and data. HTTP separates headers from bodies. SQL has prepared statements. Even email distinguishes the envelope from the message. The Model Context Protocol (MCP), the fast-growing standard for connecting AI agents to external services, inherits that gap from the models it sits on top of.

Your legal team just handed you a 400-page document and said "figure out compliance." The EU AI Act is live, your organization falls under its scope, which is broader than many expect. Even non‑EU companies must comply if their AI systems are used, deployed, or produce effects within the European Union. In practice, that means that global organizations building or integrating AI models cannot treat the Act as a regional regulation.

AI risk doesn’t live in the model. It lives in the APIs behind it. Every AI interaction triggers a chain of API calls across your environment. Many of those APIs aren’t documented or tracked. That’s your real exposure. Shadow API discovery gives you visibility into those hidden endpoints, so you can find them before attackers do. If you don’t know which APIs your AI relies on, you can’t secure the system.

Dimitris Georgiou has been a self-professed computer geek since the early 80s. At university, he studied the convergence of educational technology with computer science as part of his psychology MA – finding, to his disbelief, that systems were perilously insecure. Since then, he’s always worked in and around cybersecurity.

Your board wants AI. Your developers are building with it. Your budget committee is asking for an ROI timeline. But as CISO, you're the one who has to answer when the inevitable question comes up: "How do we know this is secure?" If you're like most security leaders, you're caught between two impossible positions. Say yes to AI initiatives without proper security controls, and you're responsible when something goes wrong.

AI systems are no longer just isolated models responding to human prompts. In modern production environments, they are increasingly chained together – delegating tasks, calling tools, and coordinating decisions with limited or no human oversight. Almost all that communication happens through APIs. This shift offers enormous productivity benefits. But it has also complicated security. Because as soon as systems can talk to each other, they can be attacked through each other.

Broken authorization is one of the most widely known API vulnerabilities. It features in the OWASP Top 10, AppSec conversations, and secure coding guidelines. Broken Object Level Authorization (BOLA) and Broken Function Level Authorization (BFLA) account for hundreds of API vulnerabilities every quarter. According to the 2026 API ThreatStats report, authorization issues ranked ninth in the API Top 10, “reflecting chronic difficulty in managing roles and permissions at scale.”